Early life height and weight production functions
with endogenous energy and protein inputs
Esteban Puentes
a,1,*
, Fan Wang
b, Jere R. Behrman
c, Flavio Cunha
d,
John Hoddinott
e, John A. Maluccio
f, Linda S. Adair
g, Judith B. Borja
h,
Reynaldo Martorell
i, Aryeh D. Stein
ia
Department of Economics, Universidad de Chile, Chile b
Department of Economics, University of Houston, United States c
Departments of Economics and Sociology and Population Studies Center, University of Pennsylvania, United States d
Department of Economics, Rice University, United States
eDivision of Nutritional Sciences and the Charles H. Dyson School of Applied Economics and Management, Cornell University and International Food Policy Research Institute, United States
f
Department of Economics, Middlebury College, United States g
Department of Nutrition, University of North Carolina, United States h
USC-Office of Population Studies Foundation, Inc and Department of Nutrition and Dietetics, University of San Carlos, Cebu, Philippines i
Rollins School of Public Health, Emory University, United States
1. Introduction
Inadequate child growth and weight gain are of paramount concern. Approximately 165 million children under five years old in developing countries are stunted and
100 million are underweight (Black et al. (2013)). Growing
evidence indicates that early-life undernutrition is associ-ated with, and likely in part causes, reduced education, adult
cognitive skills, and wages (Grantham-McGregor et al.,
A R T I C L E I N F O
Article history: Received 20 June 2015
Received in revised form 21 February 2016 Accepted 1 March 2016
Available online 11 March 2016
JEL classification: I12
O15 C13
Keywords: Nutrition Early childhood Endogeneity of inputs Growth
Proteins
A B S T R A C T
We examine effects of protein and energy intakes on height and weight growth for children between 6 and 24 months old in Guatemala and the Philippines. Using instrumental variables to control for endogeneity and estimating multiple specifications, we find that protein intake plays an important and positive role in height and weight growth in the 6–24 month period. Energy from other macronutrients, however, does not have a robust relation with these two anthropometric measures. Our estimates indicate that in contexts with substantial child undernutrition, increases in protein-rich food intake in the first 24 months can have important growth effects, which previous studies indicate are related significantly to a range of outcomes over the life cycle.
ß2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
* Corresponding author at: Diagonal Paraguay 257, of 1501, 8330015 Chile. Tel.: +56 2 29783455; fax: +56 2 29783413.
E-mail address:[email protected](E. Puentes). 1
The authors thank reviewers on previous versions for useful comments and Grand Challenges Canada (Grant 0072-03), Bill & Melinda Gates Foundation (Global Health Grant OPP1032713), and the Eunice Shriver Kennedy National Institute of Child Health and Development (Grant R01 HD070993) for financial support. The funders have no involvement in any part of the research project.
Contents lists available atScienceDirect
Economics and Human Biology
j o u r n a l h o m e p a g e : h t t p : / / w w w . e l s e v i e r . c o m / l o c a t e / e h b
http://dx.doi.org/10.1016/j.ehb.2016.03.002
2007; Engle et al., 2007, 2011; Victora et al., 2008; Hoddinott et al., 2008, 2013; Behrman et al., 2009; Maluccio et al., 2009).
Despite widespread concern about early-life undernu-trition there is limited systematic knowledge about production technologies for key outcomes, particularly height and weight, needed to inform more-effective program and policy design. This gap is partially due to inherent difficulties in modeling these complex biological and behavioral processes—often strong assumptions are required for estimation, so that it is difficult to make definitive conclusions. A major challenge in estimating production functions for height and weight is that inputs reflect behavioral choices. Using data from the same
Philippine study analyzed in this paper,Akin et al. (1992)
andLiu et al. (2009)find that families allocate nutrients to compensate for prior poor health. Where allocations reflect compensatory behaviors that are not controlled for in the estimation, the estimated effect of nutrients on growth can be biased.
Another challenge is measurement error in inputs.
Using related data from Guatemala,Griffen (2016)finds
that estimates of energy effects on height are substantially larger using instrumental variables (IV) than with ordinary least squares (OLS) probably in part due to measurement error.
In this paper, we examine relations between energy intake and: (1) linear growth and (2) weight gain. We use longitudinal data from Guatemala and the Philippines that includes detailed information on anthropometric out-comes, nutrition and other inputs collected at intervals of two-three months to estimate height and weight produc-tion funcproduc-tions for children in the critical age range 6–24 months. In our specifications, height and weight depend on lagged height and weight, energy intakes, breastfeeding, diarrhea, and individual fixed endowments. We combine individual fixed-effects (FE) with instrumental variables (IV) to control for both endogeneity and measurement error.
This paper presents three important methodological contributions. First, we estimate production functions for two countries, Guatemala and the Philippines, and for two anthropometric measures, height and weight, which allows us to compare the robustness of our findings across different settings and anthropometric outcomes. Second, we improve on previous IV literature on growth by providing details of instrument selection and an assessment of how the results are robust to changes in the instrument set. We present estimates for numerous instrument combinations, putting emphasis on those judged more reliable based on over-identification and weak instrument tests. Third, in addition to considering total energy intake, which is the nutritional input usually considered in the economics literature, we disaggrega-teenergy intake into two components: proteins and (all) other macronutrients (which we refer to as ‘‘non-proteins’’, meaning fat and carbohydrates). This emphasis on dietary
quality, highlighted by Arimond and Ruel (2004), is
especially relevant because it may help design interventions that better reduce stunting and underweight. We find robust and positive effects of proteins on height and weight
growth. Energy from other macronutrient consumption (non-proteins), is not systematically related to these anthropometric measures, which suggests that protein-rich foods are particularly important for growth of undernour-ished children.
2. Specifications of height and weight production functions and identification
2.1. Input selection
Our choice of inputs is guided byBlack et al. (2008)who
argue that inadequate diet and disease are the main immediate causes of stunting and wasting. With respect to diet, two energy sources have been identified as being especially important for child growth: proteins and non-protein energy from other macronutrients. Infants require certain minimum amounts of energy and proteins to maintain long-term good health but these requirements are heterogeneous and depend on several factors including
weight and whether the child is breastfed (FAO, 2001;
WHO, 2007). Children’s energy requirements are partly driven by energy costs of linear growth, which has two components: (1) energy needed to synthesize growing
tissues and (2) energy stored in these tissues (FAO, 2001).
These comprise approximately one-third of total energy requirements during the first three months of life, but despite increasing in absolute terms they decline to only 3% by age 24 months, in part because overall energy requirements increase substantially with body size. Proteins are needed to balance nitrogen loss, maintain the body’s muscle mass, and fulfill needs related to tissue
deposition (WHO, 2007). There is also evidence from
research on animals that protein provides anabolic drive
for linear bone growth (WHO, 2007).2
To study the relative importance of protein and non-protein sources, we first examine the relationship between total energy and height and weight and then consider the potential for separate roles of the two at once in a single growth model. The comparison of proteins with non-proteins highlights the relative importance of non-proteins in children’s diets and informs what types of interventions
might have greater impact on height and weight.3There is
a limited literature focused on the distinction between
total energy and protein energy.Pucilowska et al. (1993)
find that high-protein supplementation in Bangladeshi children with shigellosis, a severe bacterial disease, increased weight compared to normal protein diets. A randomized evaluation for children up to 2 years of age in several European countries demonstrated that receiving baby formula with high protein content (% calories from
protein) increased weight, but not height (Koletzko et al.
(2009)). Both of these study populations, however, are different from the ones we examine. The Bangladeshi
2Micronutrients also play important roles in tissue building (WHO,
2007), but there is limited information about them in our data. Hence our focus on protein and non-protein energy.
3
sample is restricted to children recovering from shigello-siswhile the European sample had not experienced the same nutritional deficiencies found in our samples.
Using a sample more similar to ours,Moradi (2010)finds
that access to high-quality protein, such as from livestock farming, better predicts height in some African
countries than other energy sources. Similarly,Baten and
Blum (2014), using global information for the first part of the twentieth century, that includes Guatemala and the Philippines, also find that local availability of cattle, milk and meat were an important predictor of adult
height.4
A related issue is protein quality. Proteins are composed of amino acids with specific cell functions, and amino acid content defines protein quality. For instance, plant-based proteins lack essential amino acids
unlike animal-based proteins (Dewey, 2013). In addition,
plant-based diets have high levels of phytic acid, which
might inhibit zinc absorption (Gibson, 2006), and zinc
plays a key role in cellular growth and differentiation (Imdad and Bhutta, 2011). For animal-based protein, Mølgaard et al. (2011)argue that dairy intake has positive impacts on child growth. Although the mechanism is not entirely clear, this may be due to the stimulating effect
on plasma insulin-like growth factor (IGF-1) (Michaelsen,
2013).
Breastfeeding is another critically important source of
nutrition in early life (Black et al., 2013). In this paper, we
have data on breastfeeding status but not on the amount of breast milk consumed. Thus, our energy intake measures exclude energy from breastmilk requiring us to control for breastfeeding status in the models.
Among diseases that affect growth, Walker et al.
(2011) suggest that persistent diarrhea and other diseases can have long-lasting effects on children’s physical development. Therefore, in our analyses, we incorporate diarrhea as an input, as it is considered a major contributor to stunting, wasting and child
mortality (Black et al., 2013).
2.2. Height and weight production functions
The main challenges for estimating height and weight production functions include the endogeneity of inputs
and measurement error (Behrman and Deolalikar, 1988).
To overcome these, we follow the general approach developed in recent research on production function
estimation for cognitive and non-cognitive skills (Todd
and Wolpin, 2003, 2007; Cunha and Heckman, 2007).
Lethi,tdenote childiheight at aget,wi,tweight at aget
andxi,jthe input (e.g., proteins, non-proteins, or disease)
at age j (For simplicity, we present the model with a
single input but generalization to several inputs is
straightforward.). Fairly general height and weight production functions are:
hi;t¼
am
iþXt
j¼1
b
tjxi;jþ 2hi;t (1)wi;t¼
sm
iþXt
j¼1
d
tjxi;jþ 2wi;t (2)where
m
iis an individual fixed effect (including geneticendowments and fixed parental and household
character-istics) and 2h
i;tand 2wi;tare error terms. This formulation
allows the entire input history to enter into both equations
up to timet. Furthermore, it allows for impacts of past
inputs on current height and weight and for the possibility that such impacts differ by age. This approach also distinguishes our work from other studies using the same
data.Griffen (2016)relies on the fairly strong assumption
that past inputs have constant effects on height in Guatemala, so that history plays little role in growth.
Similarly, height production functions estimated byde Cao
(2015) in the Philippines, assume that height growth depends only on current inputs.
Because they include individual fixed effects and the
entire input history, Eqs. (1) and (2) are difficult to
estimate. For example, if inputs are treated as endoge-nous and an IV approach were used, it would be necessary to have at least one instrument for each period in the entire input history. Thus, instead of directly estimating these two equations, we make two further assumptions that allow less demanding specifi-cations in terms of data and instrument requirements, while remaining more flexible than previous specifica-tions in the literature.
Assumption 1. Effects of past inputs follow a monotonic
(likely decreasing) pattern at a constant rate
g
for eachperiod.5That is:
b
tj=
gb
t1jandd
tj=gd
t1j.Assumption 2. The coefficients on inputs in the height
function are the same as those in the weight function, up to
a multiplicative constant
d
t1j= ((1 +s
)/a
)b
t1j.Together, these assumptions reduce the set of endoge-nous variables to a tractable number, thereby reducing the number of required instrumental variables.
From Eq.(1)and taking first-differences in height we
obtain:
D
hi;t¼b
0xi;tþXt1
j¼1
ð
b
tjb
t1jÞxi;jþ 2hi;t 2 h i;t1Incorporating the first assumption that
b
tj=gb
t1j,we obtain:
D
hi;t¼b
0xi;tþ ðg
1ÞXt1
j¼1
b
t1jxi;jþ 2hi;t 2 h i;t14
Relatedly, and using the same data from the Philippines that we use,
Bhargava (2016)studies the association of macronutrients (proteins) and micronutrients (calcium) with anthropometrics, finding that both, protein and calcium are strongly associated with height and weight in the first 24 months of life and also on adolescence. However,Bhargava (2016)only controls for individual effects, assuming several time varying variables as exogenous.
Next, consider the difference in Eqs.(1) and (2)(after
cross multiplication with
s
anda
):a
wi;t1s
hi;t1¼ Xt1j¼1
ð
ad
t1jsb
t1jÞxi;jþ
a
2wi;t1s
2 h i;t1Under the second assumption that
d
t1j= ((1 +s
)/a
)b
t1j, we have:a
wi;t1s
hi;t1þs
2hi;t1a
2 w i;t1¼Xt1
j¼1
b
t1jxi;jConsequently,
D
hi;t¼b
0xi;tþa
ðg
1Þwi;t1s
ðg
1Þhi;t1þv
Dhi;t (3)where
v
Dhi;t ¼ 2 h
i;tþ ð
s
ðg
1Þ1Þ 2 hi;t1
a
ðg
1Þ 2 w i;t1. Under these assumptions, height growth can be expressed as a function of current inputs, past heightand weight, and an error involving current (t) and previous
period (t1) shocks. Current inputs enter directly; the full
history of past inputs enter indirectly through the lagged height and weight.
We proceed in similar fashion for weight and obtain:
D
wi;t¼d
0xi;tþ ðg
1Þð1þ
s
Þwi;t1s
ð
g
1Þð1þs
Þa
hi;t1þv
Dwi;t (4)where
v
Dwi;t ¼ 2 w i;tþ
sðg1Þð1þsÞ
a 2hi;t1½ð
g
1Þð1þs
Þ þ1 2 w i;t1.As with the change-in-height Eq.(3), the
change-in-weight Eq.(4)depends on current inputs, past height and
weight, and an error including current and previous period
shocks.6
This framework forms the core of our approach to estimating production functions for height and weight.
Estimation of Eqs.(3) and (4) allow recovering
b0
fromEq.(1)and
d0
from(2).2.3. Estimation and identification
Although differencing removes individual-level fixed effects and thus controls for important sources of potential bias (unobserved persistent heterogeneity including, e.g., genetic endowments and fixed parental and household characteristics), to consistently estimate the parameters in
the relations for change in height (Eq.(3)) and change in
weight (Eq. (4)), we still need to overcome several
endogeneity problems. First, by construction previous height and weight are correlated with the error terms of Eqs.(3) and (4) (see Eqs.(1) and (2)). Moreover, if we assume that the household responds to past shocks as is likely and for which there is evidence for the Philippines (Akin et al., 1992; Liu et al., 2009), current inputs may be correlated with the error terms.
We address potential endogeneity by using IV, which
also addresses bias due to random measurement error inx
under the assumption that the instruments are uncorre-lated with that measurement error. The set of candidate instruments we use differs by country but draws on plausibly exogenous factors including a randomized intervention in Guatemala and prices of common foods in both countries. We treat market prices as exogenous to
households (as in Liu et al. (2009)). Using prices as
instruments for inputs is a well-established approach in
the estimation of production functions (Todd and Wolpin
(2003)). We also include past height and weight measures,
hi,t2andwi,t2as instruments to help identify the effects
of lagged height and weight. (Instruments are described in
further detail in Section3.3.)
Using the available instruments, we endogenize protein and non-protein intakes, as well as lagged height and weight. However, we do not have access to instruments in both countries that also would allow us to control for the
potential endogeneity of breastfeeding or diarrhea.7
Controlling for individual-level fixed effects is an impor-tant aspect of our approach, however, and goes part way toward addressing their potential endogeneity. For exam-ple, fixed effects control for the possibility that certain children have a pre-disposition for diarrhea, or live in particularly unsanitary households. However, if house-holds change breastfeeding practices when health shocks affect their children’s health or change sanitary conditions to reduce the diarrhea prevalence, the estimated effects of breastfeeding and diarrhea could be downward-biased. For instance, households that have increased breastfeeding could be compensating for negative health shocks, suggesting a negative relationship between growth and breastfeeding, while correcting for endogeneity could show a positive relationship (and similarly for diarrhea). Because our principal objective is to study the roles of proteins and non-proteins in the production functions, however, we do not emphasize the coefficients for diarrhea and breastfeeding but instead make clear the assumptions under which our primary coefficients of interest are
consistently estimated even ifbreastfeeding or diarrhea
are endogenous in the model. Our estimation approach is consistent provided the instruments are not correlated with the error term in the production function, conditional on breastfeeding and diarrhea as well as other covariates mentioned below. This is plausible for the same reason that the instruments are exogenous in relation to the energy inputs, e.g., that they are not correlated with
individual-level time-varying health shocks.8
In principle, there also could be interactions among inputs in the production function, such as between
6
Specifications of the change-in-height equation that exclude lagged weight, and the change-in-weight equation that exclude lagged height were also estimated. Results were similar to the more general specification (available on request).
7
Previous work using the Philippine data has used rainfall as an instrument for diarrhea (Akin et al., 1992). We attempted to endogenize diarrhea using spatial and temporal variation in rainfall and temperature as instruments in Guatemala, but they had minimal predictive power. To keep the structure parallel across the countries, we do not use rainfall to endogenize diarrhea in either country.
8
nutrient intakes and diarrhea, or between breastfeeding and other nutrient intakes but a specification incorporat-ing such interactions would be even more challengincorporat-ing to estimate, requiring additional instruments. Given that there are already four variables that we treat as endoge-nous in our main models (protein, non-protein, lagged height, and lagged weight), we do not estimate models with such potential interactions; instead, we studied possible interactions by splitting the sample. For instance, to examine whether diarrhea or breastfeeding interacts with diets, we estimated specifications for the sample that is breastfed and compare the results with the sample that is not breastfed. We carried out a similar exercise for diarrhea. Our results indicate that coefficients are not affected when we separate the sample by breastfeeding types. For diarrhea, there was some evidence of interaction effects, where diarrhea lowers the effects of macronu-trients, but because most of the specifications suffer from problems of weak instruments, we are unable to draw strong conclusions.
The estimation of the growth equations also includes an indicator for whether the child was female, number of days since the previous measurement, and age and age squared
at timet.
Our methods permit us to improve upon the previous literature that investigates the effects of total energy on anthropometrics. Since we do not have a single set of preferred instruments, we are able to robustly study effects of total energy on height and weight across two settings. We do this estimating the changes in height and weight, first using total energy intakes and then separating protein and energy from other macronutrient intakes to examine their relative partial effects in each model.
The final estimating equations for the change in each
anthropometric measureAi,tthat we estimate, adding the
additional controls to Eqs.(3) and (4), are:
D
Ai;t¼l
AenergyEi;tþr
1Awi;t1þr
A2hi;t1þ
r
A3days no diari;tþ
r
A4bfi;tþr
A5agei;tþ
r
A 6age2 i;tþ
r
A
7femalei;tþ
r
A8gap msmti;tþh
DAi;t (5)and
D
Ai;t¼l
AprotProti;tþl
Anon protNon Proti;tþd
A1wi;t1þ
d
A2hi;t1þd
A3days no diari;tþ
d
A 4bfi;t
þ
d
A5agei;tþd
A 6age2i;tþd
A
7femalei;t
þ
d
A8gap msmti;tþn
DAi;t (6)whereAi,tis either weight (wi,t) or height (hi,t) of childiat
aget;Ei,t,Proti,t,Non_Proti,tcorrespond to the total energy
intake, protein intake and non-protein intake;
days_no_-diari,t is the number of days without diarrhea between
measurements;bfi,tis a dummy variable equal 1 if the child
was breastfed during the period leading up to aget;agei,t
and age2
i;t are age and age squared;femalei,tis a dummy
variable equal to 1 if the child is a female; andgap_msmti,t
is the number of days between measurements. Finally, the
error terms in Eqs.(3) and (4)exhibit serial correlation of
order one by construction. We use cluster standard errors
at the individual level to take into account this serial correlation, and also any possible correlation of individual error terms; using cluster standard errors is more general than a correction for serial correlation. Additionally the error terms are correlated between equations so there are possible efficiency gains of estimating a system of equations. Nonetheless given the already complex nature of the estimation, we estimate single equations. The cluster errors we calculate, therefore, can be seen as an upper bound of the standard errors.
3. Data
Estimation of (5) and (6) requires high-frequency longitudinal data in early life that contain information
on the outcomes (height9and weight) and inputs (proteins
and other macronutrients, breastfeeding, and diarrhea), as well as plausibly exogenous instruments. We now describe the data and contexts for two unique studies that fulfill these substantial requirements relatively well, one in Guatemala from the 1970s and the other in the Philippines from the 1980s.
3.1. Guatemala
We use data from The Institute of Nutrition of Central America and Panama (INCAP) 1969–1977 nutritional supplementation trial. Four rural villages from eastern
Guatemala were selected, one relatively large pair (900
residents) and one smaller pair (500 residents). At the
outset, the villages were similar in terms of child nutritional status, measured as height at age three years, and were highly malnourished with over 50% of children
severely stunted, i.e., with height-for-agez-score<3. One
large and one small village were randomly selected to receive a high-protein supplement (Atole); the others received an alternative supplement devoid of protein (Fresco). A 180 ml serving of Atole contained 11.5 grams of protein and 163 kcal. Fresco had no protein and a 180 ml serving had 59 kcal. The main hypothesis was that increased protein would accelerate mental development; additionally, it was expected that the high-protein nutritional supplement would affect physical growth. The nutritional supplements were distributed in
central-ly-located feeding centers in each village (Habicht et al.,
1995). Virtually all (>98%) families participated (Martorell
et al. (1995)).
From 1969 to 1977, anthropometric measures (height and weight) were taken every three months for all children 24 months of age or under (including newborns entering the study) in the four villages. This yields a maximum usable sample for our analyses of 878 children measured at least twice by the age of 24 months. The amount of supplement intake was recorded daily in all villages. Home dietary information was collected every three months, including the types and amounts (except for breastmilk) of
9
all foods and liquids consumed. These dietary histories were based on a 24-h recall period in the larger villages and a 72-h period in the smaller villages (from which we construct daily averages), and permit calculation of protein and non-protein intakes for the 24-h period by summing the nutritional content for each food item. The survey recorded the total months a child was breastfed. Nutrients from breastfeeding were not included in the nutritional intake calculations. Retrospective information on illness, specifically the length in days of episodes of diarrhea and fever, was collected semi-monthly.
3.2. The Philippines
We use the Cebu Longitudinal Health and Nutritional Survey, a survey of Filipino children born between May 1983 and April 1984 in 33 rural and urban communities (barangays) in Metropolitan Cebu. The baseline survey included 3327 women sampled at a median of 30 weeks of gestation, and yielded a sample of 3080 singleton live births. This sample also exhibits high levels of undernutrition; at age 24 months, 62% of the children were stunted and 32% underweight. During the first two years of each child’s life, data were collected every two months. This included anthropometric measurements, 24-h dietary recall of types and amounts (except breast milk) of all foods and liquids eaten, breastfeeding, and recent illness history. For breastfed children, the survey also collected the frequency and length of time spent breastfeeding. Total protein and energy intakes were calculated from foods consumed the previous day (24-h recall method). At each survey, mothers reported whether the child had diarrhea in the past 24 h, and if so, when the episode began, and the number of days the
child had diarrhea during the previous week (Adair et al.,
2011). The maximum usable sample of children between 6
and 24 months of age for the Philippines is 2713.
3.3. Variable construction
Linear growth and weight gain are calculated as the difference between consecutive measurements. Although measurements were scheduled at specified intervals (every three months in Guatemala, every two in the Philippines), there were deviations including instances where a scheduled measurement did not occur. Because children experience high growth and growth spurts during the first two years of life, even differences of several days can be associated with significant differences in growth. We account for this by controlling for the exact number of days between measurements.
Ideal data for this analysis would have information on
protein and non-protein intakes over the entire period
between measurements, but even in these uniquely comprehensive studies such detailed information is not available. Therefore, we approximate intakes over the entire period by using the average of the 24-h intakes calculated from the dietary recall information at the beginning and end of each period (which decreases measurement error relative to using only one point in time) multiplied by the exact number of days between measurements. For Guatemala, we add to this figure the
intakes from the supplement (which were measured daily throughout the period) to obtain total protein and other
intakes (as well as their sum, measured as total energy).10
For breastfeeding, we create a dummy indicator for whether the child was breastfed in the month previous to
measurement at timet. While this does not fully exploit
the detailed information available for the Philippines, it is done to have similar specifications across countries.
The final input we include is diarrhea. For Guatemala, the protocol was to collect information every 15 days, so it is possible to construct the number of days experiencing diarrhea for the complete periods between anthropometric
measurements.11For the Philippines, it is only possible to
construct the number of days with diarrhea during the week previous to each bimonthly anthropometric mea-surement. To extrapolate this to the full period between measurements, we estimate a count model for number of days with diarrhea for each two-month period with the Guatemalan data and use the estimated parameters from that model to predict number of days each Filipino child
had diarrhea in each two-month period.12
As outlined in Section2.3, in our main specifications we
instrument for protein, other macronutrient intakes, and lagged height and weight. We now describe in detail the other instruments besides twice lagged height and weight. In both countries we use unit prices for various food items, selected with emphasis on foods with high protein content and/or important in the local diet. For Guatemala, prices are averages of national-level prices measured during December each year. We use lagged prices of eggs, chicken, pork, beef, dry beans, corn, and rice. Unit price variables for Guatemala are deflated and measured over the eight-year study period. For the Philippines, we use community-specific prices collected as part of the broader study. Between January 1983 and May 1986, enumerators visited two stores in each community, every other month, and collected prices (and quantity units) for a list of items. Not all items, however, were sold at each store at each visit. Consequently, there is not a complete set of prices for each item from each store (or even from each community in instances where no price was available from either store) in each measurement period. We selected as instruments the prices of dried fish, eggs, corn and tomatoes since these
are the ones with the highest frequency in the sample.13
We use both current and lagged prices of those selected food items. By estimating a large set of instrument combinations, our approach does not depend on any one particular price, avoiding subjective instrument selection. For Guatemala, we also exploit the experimental variation resulting from the randomized allocation. We
10
For Guatemala we use an individual-level fixed-effects model to impute nutrient intakes for approximately 5% of missing observations. See Data Appendix Section 1.
11
Approximately 45% of such 15-day visits were missed. In those instances, we assume the child had similar diarrhea patterns across all 15-day intervals during that growth period and scale-up the observed number of days accordingly.
12
See Data Appendix Section 2 for details of the estimation of the count model for diarrhea.
13
use a dummy variable that indicates whether the village had a feeding center that provided the high-protein supplement. We also interact this indicator with the distance from the home of the child to that feeding center. While the presence of a randomized allocation of a high-protein supplement provides an important source of exogenous variation, since there are four endogenous variables, additional instrumental variables also are used, i.e., twice lagged anthropometrics and food prices. For the Philippines we rely on price variation, which, unlike the annual Guatemalan food price data, varies both within-years and spatially, with information on these food items for the majority of measurement periods and each of the 33 communities.
3.4. Descriptive statistics
Over the period from ages 6 to 24 months, each Guatemalan child is observed an average of 4.3 times and each Filipino child 9.1 times. The sample we describe includes all observations (measurements of children at different ages) with complete information for the follow-ing variables: change in height between consecutive measurement periods (linear growth), change in weight between consecutive periods (weight gain), total energy, energy from protein, energy from non-protein,
breastfeed-ing indicator, and days with diarrhea.14The final number of
observations used in each specification varies depending on the availability of the instrumental variables used in that specification, since instruments for some observations are missing.
Table 1compares the main variables for both samples. On average and at all ages, the Filipino children in the early 1980s were taller than the Guatemalan children in the 1970s. For example, at 12 months of age, Filipino children were on average 70.7 cm tall, while their Guatemalan counterparts were 1.8 cm shorter. In terms of average weight, however, there were no significant differences between countries—at 24 months, children from both countries averaged 9.8 kg. 44% of the Guatemalan children were stunted, and 27% underweight. The corresponding levels were lower, 25% and 11%, for Filipino children. In 2011 for low- and middle-income countries, average levels of stunting were 28% and of underweight 17%, and 36% and
18% in Africa (Black et al., 2013). With broadly similar
levels of stunting and underweight, thus, our historical samples remain relevant to understanding undernutrition in many countries and regions.
Table 2shows that Guatemalan children appear more likely to have been breastfed at all ages. In both countries, breastfeeding declines with age. At six months, 99% of Guatemalan children were breastfed, while at 24 months only 18% were; the proportions were 76% and 14% for Filipino children.
Patterns between diarrhea and age are less clear. In Guatemala, average number of days with diarrhea (per 3-month measurement period) increases with age to 15 months, after which it declines. Levels are relatively lower in the Philippines, fluctuating between about 2 and 6 days (per 2-month period), with no clear age pattern.
For Guatemala, information is complete on all of the instruments except the distance to the feeding center, which
is missing for5% of observations. For the Philippines, on
the other hand, incomplete price availability leads to larger reductions in the sample size. The potential sample has 24,820 child-age observations; the lagged price of corn, which is the most complete, has 18,710 observations and the lagged price of tomatoes, the least complete, has 16,084 observations.
4. Results
4.1. Overview
We estimate height and production functions for children 6–24 months, the period widely considered to be a critical window for post-birth nutritional
invest-ment.15We use Generalized Method of Moments (GMM)
for exactly-identified models and Limited Information Maximum Likelihood (LIML) for over-identified models because the latter allows for smaller finite-sample bias (Stock and Yogo, 2005). As noted, we cluster error terms at the individual level to take into account correlation of
individual error terms and serial correlation (Baum et al.,
2007).16We first estimate height and weight production
functions using only total energy (i.e., the sum of calories from protein and other sources), then we analyze separately the roles of proteins and non-proteins. In all specifications, the energy intakes, lagged height, and lagged weight are treated as endogenous, and we control for breastfeeding, number of days without diarrhea since the previous measurement, child sex, number of days since the previous measurement, and age and age squared.
Because there are many potential instrument combina-tions, to establish general results that do not depend on one specific instrument combination, we estimated large subsets of all possible combinations. For Guatemala we first restricted the instrument sets to combinations that always had the Atole experiment indicator. Then, we
14For the Philippines, the number of available observations is constant across variables, but decreases with child age due to attrition. For Guatemala, the number of children with available information on intakes and diarrhea is smaller than the number with anthropometric measures because the dietary and morbidity information for infants under 12 months was not collected until 1973.
15
There are additional substantive, as well as practical, reasons for the 6–24 month window. First, during the first six months most infants are breastfed; indeed WHO recommends exclusive breastfeeding from birth to age six months. Therefore, before that age proteins and non-proteins in the diet reflect non-exclusive breastfeeding that could be detrimental to growth. Second, it is not possible to study the production function at earlier ages because our final specification models growth and the candidate instrumental variables include second lags of height and weight (Section2.2). Because we model growth and use these second lags, however, the analysis does incorporate information on individuals prior to six months of age. Third, while the frequency of measurements differs, both samples have measurements at ages six and 24 months, facilitating comparability.
16
systematically varied inclusion of distance interactions with Atole indicator, second lags of height, second lags of weight, and from two to four of the seven food prices (eggs, chicken, pork, beef, rice, beans and corn). For the Philippines, we systematically varied inclusion of second lags of height, second lags of weight, and from two to six of the eight (four current and four lagged) food prices (eggs, fish, tomatoes and corn). A summary of our instrument
combinations is found in theData Appendix Section 6. For
Guatemala, there are 546 specifications (i.e., each with a different instrument set) for the version of the model with
total energy (Eq. (5)) and 525 when proteins and
non-proteins are included separately (Eq. (6)).17 The total
number of specifications estimated for the Philippines is 602 for both models.
For each specification, we calculate the robust versionsof the Hansen-J (HJ) over-identification test, the
Anderson–Rubin under-identification test (Anderson and
Rubin, 1949), and the WaldF-statistic (robust Cragg–Donald or CD statistic) to detect weak instruments. Since our main models have four endogenous variables and we estimate them assuming heterokedasticity, it is not possible to
compare CD statistics with critical values fromStock and
Yogo (2005). The robust versions of these tests were
developed inKleibergen and Paap (2006). We also calculate
for each endogenous variable Angrist and Pischke’s (AP)
partial F (Angrist and Pischke, 2009), which are informative
about the presence of weak instruments. Finally, for all over-identified models we calculate the Hausman test of equality of OLS and IV estimates.
We use the HJ over-identification and the CD statistics to focus our analysis on specifications with stronger and more exogenous instruments. In general, the Anderson–Rubin and Hausman tests strongly support our identification strategy. Based on the Anderson–Rubin test, we reject under-identification in all specifications for Guatemala, while for the Philippines we reject under-identification in 96% of the specifications. The Hausman test rejects equality of OLS and IV estimates in 99% of the specifications with total Table 1
Guatemala, nutritional outcomes and inputs.
Height (cm) Change in
height
Weight (grams)
Change in weight
Total energy (kcal)
Non-protein (kcal)
Protein (grams)
Mean (sd) Mean (sd) Mean (sd) Mean (sd) Mean (sd) Mean (sd) Mean
(sd)
6 months 62.97 5.19 6871.99 1424.26 131.86 113.82 4.51
(2.38) (1.34) (959.14) (470.80) (149.30) (131.73) (5.45)
9 months 66.21 3.46 7516.29 698.85 218.16 191.06 6.77
(2.69) (1.55) (1085.65) (469.85) (193.81) (170.68) (6.85)
12 months 68.91 2.96 7979.84 500.85 340.90 301.12 9.95
(3.00) (1.47) (1147.19) (463.02) (232.77) (206.26) (7.78)
15 months 71.01 2.40 8292.93 461.96 511.06 451.65 14.85
(3.21) (1.35) (1117.15) (432.51) (245.47) (218.69) (8.39)
18 months 73.25 2.29 8712.95 431.83 656.70 581.27 18.86
(3.36) (1.41) (1118.61) (495.17) (271.83) (241.61) (9.29)
21 months 75.47 2.33 9186.83 505.42 767.85 678.13 22.43
(3.47) (1.39) (1129.93) (481.52) (293.42) (261.17) (10.08)
24 months 77.53 2.23 9752.69 604.67 847.75 747.65 25.03
(3.55) (1.44) (1168.04) (523.06) (303.51) (271.84) (10.37)
Observations 3802 3802 3802 3802 3802 3802 3802
Philippines, nutritional outcomes and inputs
6 months 64.27 3.26 6856.72 736.13 204.93 182.57 5.59
(2.57) (1.66) (903.05) (415.00) (249.68) (221.73) (7.52)
8 months 66.80 2.54 7302.63 440.11 285.67 254.65 7.76
(2.71) (1.42) (964.47) (383.29) (279.54) (246.70) (8.99)
10 months 68.92 2.13 7642.79 338.95 349.93 312.18 9.44
(2.80) (1.39) (1028.15) (402.86) (300.60) (264.36) (10.12)
12 months 70.72 1.82 7948.05 300.68 407.33 362.27 11.27
(2.96) (1.29) (1079.27) (391.39) (310.79) (273.21) (10.56)
14 months 72.29 1.58 8225.85 278.16 477.29 423.20 13.52
(3.07) (1.22) (1115.39) (377.09) (325.60) (284.74) (11.56)
16 months 73.73 1.45 8512.16 283.81 540.50 479.13 15.34
(3.24) (1.19) (1111.83) (389.74) (328.22) (285.88) (12.22)
18 months 75.12 1.43 8797.30 286.79 589.04 521.87 16.79
(3.38) (1.23) (1143.54) (392.57) (334.33) (291.48) (12.43)
20 months 76.50 1.42 9104.64 316.95 640.35 567.20 18.29
(3.51) (1.29) (1177.77) (397.24) (347.83) (303.38) (12.74)
22 months 77.73 1.30 9436.95 338.55 681.66 603.35 19.58
(3.61) (1.33) (1210.32) (413.78) (355.37) (310.89) (12.75)
24 months 79.13 1.43 9782.39 349.09 710.41 627.28 20.78
(3.68) (1.19) (1233.11) (418.55) (354.38) (309.68) (12.99)
Observations 24,820 24,820 24,820 24,820 24,820 24,820 24,820
17
energy and 90% of the specifications with protein and non-protein separate in Guatemala and 87% and 98%, respec-tively, for the Philippines. Finally, we calculate the AP partial
F statistic for the energy coefficient (
l
henergyandl
w
energy) from
Eq. (5)and the protein (
l
hprot andl
w
prot) and non-protein
coefficients (
l
hnon prot andl
w
non prot) from Eq. (6). These statistics are useful to make comparisons across equations and variables, but do not provide formal statistical support against weak instruments, since there are no critical values available for them. In general, the results suggest that the instruments are stronger for Guatemala: the AP partial F tends to be over 30 for the protein coefficients and over 7 for energy and non-protein coefficients. For the Philippines, the AP partial F for the total energy coefficient tends to be over 20. However, it is mostly below 5 for the protein and non-protein coefficients, which suggests that instruments are
weaker in the more general specification for the
Philippines.18 Despite these differences in AP statistics,
results are broadly similar across countries, which suggests
that we are identifying structural relationships between nutrients and anthropometrics.
Since each production function is estimated multiple times, we explore distributions of estimated coefficients rather than a single or small set of ‘‘preferred’’ specifica-tions, allowing us to draw more general conclusions. We do not choose or define a preferred specification because there are no obvious criteria for doing so and because of the concern that any potential preferred specification would not be robust to changes in the set of instruments. Although a priori the instruments we propose are plausibly exogenous and strong, we put relatively more confidence in those instrument sets that better satisfy over-identifi-cation and weak instrument tests.
The results of each type of specification are presented in Tables 3–6andFigs. 1–3. InTables 3 and 5, andFig. 1, we
present the estimated overall energy coefficients. InTables
4 and 6 (Panels A and B), and Fig. 2, we presentthe
estimated protein coefficients, and in Tables 4 and 6
(Panels C and D), andFig. 3, the estimated non-protein
coefficients. Each table presents the 25th, 50th and 75th percentiles of the estimated coefficient distributions and, in the final two columns, the percentages of the
coefficient estimates that are significantly (p<0.05)
positive or negative. For each Panel in each table, the first row reports distributions for all estimated specifica-tions and, in subsequent rows, for specificaspecifica-tions that
are over-identified, and for those that have HJ P
-values>0.05 and CD statistics>1, 3, or 7 (provided
there are more than 10 such specifications in each case).19
These sets of specifications focus on results for which relatively strong and exogenous instruments are
avail-able. Figs. 1–3 present point estimates (and associated
95% confidence intervals) for all specifications that have
HJP-values>0.05 and CD>1 (corresponding to the third
rows inTables 3–6). The scale of the x-axis corresponds to
the natural logarithm of CD statistics and they-axis the
coefficient values.20
To facilitate interpretation of the coefficient magni-tudes, we simulate changes in height and weight when energy intakes increase ceteris paribus For this exercise,
we use the most restrictive specifications with CD>7 (or
CD>3 if there are fewer than ten specifications with
CD>7) and HJ P-values>0.05. Within that set of
specifications, we select the median coefficient and simulate effects of increasing energy intakes by 300 kcal per day, protein intakes by 10 g per day, or non-protein intakes by 250 kcal per day. Each of these is approximately one SD of respective intakes of 18-month old infants in both countries. This hypothetical daily increase is then multiplied by 90 in Guatemala and by 60 in the Philippines to approximate total intakes for a given measurement period, and then multiplied by corresponding coefficients Table 2
Guatemala, other inputs.
Breastfed Days with diarrhea
Female Time between measurement (days) Age (days) Mean (sd) Mean (sd) Mean (sd) Mean (sd) Mean (sd)
6 months 0.99 6.52 0.51 91.95 182.62
(0.12) (12.44) (0.50) (5.09) (3.66)
9 months 0.97 9.43 0.51 95.15 273.37
(0.17) (15.80) (0.50) (20.52) (4.17)
12 months 0.92 12.18 0.50 96.82 364.59
(0.28) (16.08) (0.50) (24.77) (4.88)
15 months 0.81 12.60 0.54 94.26 456.95
(0.40) (15.63) (0.50) (16.29) (4.08)
18 months 0.59 11.43 0.53 94.48 547.98
(0.49) (15.77) (0.50) (18.90) (3.51)
21 months 0.34 9.97 0.53 95.75 638.72
(0.47) (14.90) (0.50) (26.05) (3.29)
24 months 0.18 7.57 0.53 100.14 730.64
(0.38) (13.65) (0.50) (34.32) (3.21)
Observations 3802 3802 3802 3802 3802
Philippines, other inputs
6 months 0.76 1.54 0.53 61.77 186.41
(0.43) (2.76) (0.50) (8.92) (6.03)
8 months 0.72 4.38 0.53 60.23 246.59
(0.45) (4.23) (0.50) (5.84) (5.57)
10 months 0.68 3.11 0.53 62.03 307.98
(0.47) (2.35) (0.50) (8.61) (6.03)
12 months 0.62 2.38 0.53 61.72 369.10
(0.49) (2.37) (0.50) (8.93) (6.36)
14 months 0.53 5.25 0.53 61.48 430.07
(0.50) (5.15) (0.50) (8.55) (6.46)
16 months 0.44 4.98 0.53 61.36 490.90
(0.50) (5.06) (0.50) (8.92) (6.47)
18 months 0.34 1.99 0.53 61.54 551.72
(0.47) (2.35) (0.50) (9.56) (6.16)
20 months 0.26 6.22 0.53 61.45 612.72
(0.44) (6.25) (0.50) (8.86) (6.48)
22 months 0.19 2.78 0.53 60.83 673.14
(0.39) (3.51) (0.50) (8.55) (6.11)
24 months 0.14 1.83 0.53 61.59 734.06
(0.34) (2.73) (0.50) (9.03) (6.33)
Observations 24,820 24,820 24,820 24,820 24,820
18
Results available on request.
19Restricting the sample to those with HJp-values>0.10 generates similar results; see Data Appendix Section 5.
20
to obtain anthropometric changes. We call this exercise median prediction.
4.2. Guatemala
Table 3 summarizes for Guatemala distributions of coefficient estimates on total energy in the height and
weight equations, andFig. 1A and B show the coefficients
and confidence intervals for the corresponding
specifica-tions with CD>1. Total energy positively affects height
and weight changes. These positive relationships are most evident for specifications with relatively stronger and more exogenous instruments. Our findings are consistent with previous literature that uses stronger identification assumptions estimating similar relationships from the
same data sources (Habicht et al., 1995; Griffen, 2016).
For height in Guatemala, estimated coefficients on total energy are positive in the vast majority of cases, positive
and significant (p<0.05) in 35% of cases, and never
negative and significant. The positive relationship is more Table 3
Impact of total energy intake on change in heights and weights, Guatemala.
Total energy Distribution of total energy coefficient sig>0 sig<0
# of sp. p25 p50 p75 %-Sig %-Sig
Panel A: Height
All IV 546 0.0182 0.0099 0.0288 35 0
All over-identified IV 525 0.0179 0.0107 0.0289 36 0
CD>1P-val HJ>5 137 0.0090 0.0034 0.0170 15 0
CD>3P-val HJ>5 21 0.0009 0.0231 0.0438 57 0
Panel B: Weight
All IV 546 0.0061 0.0059 0.0159 36 0
All over-identified IV 525 0.0036 0.0060 0.0159 38 0
CD>1P-val HJ>5 129 0.0024 0.0050 0.0233 32 0
CD>3P-val HJ>5 36 0.0142 0.0230 0.0239 83 0
CD = Robust Kleibergen-PaapFstatistic,P-value,J=P-value of HansenJstat100.
1st column: # of specifications that meet criteria; 2nd–4th col: percentile of distribution of estimated coefficients. 5th (6th) column: percent of estimated coefficients that are positive (negative) and significant at 5% significance level.
1st row: all specifications; 2nd row: all over-identified specifications for which # of IVs># of endogenous variables. Other rows include all specifications satisfying the indicated criteria based on the CD and HJ tests.
All specifications include breastfeeding, diarrhea, sex, age, and age squared as covariates and a seasonal dummy for the Philippines, and lagged height and lagged weight, both of which are treated as endogenous.
Height coefficients are divided by 1000 for presentation purposes.
Table 4
Impact of protein and non-protein energy on change in heights and weights, Guatemala.
Protein Distribution of protein coefficient sig>0 sig<0
# of esp. p25 p50 p75 %-Sig %-Sig
Panel A: Height (protein)
All IV 525 0.0666 0.1047 0.1293 53 0
All over-identified IV 448 0.0774 0.1044 0.1268 58 0
CD>1P-val HJ>5 163 0.0931 0.1067 0.1232 77 0
CD>3P-val HJ>5 48 0.1043 0.1079 0.1106 100 0
Panel B: Weight (protein)
All IV 525 0.0541 0.0588 0.0632 92 0
All over-identified IV 448 0.0543 0.0586 0.0627 97 0
CD>1P-val HJ>5 347 0.0540 0.0571 0.0614 100 0
CD>3P-val HJ>5 132 0.0534 0.0542 0.0567 100 0
Non-protein Distribution of non-protein coefficient sig>0 sig<0
# of esp. p25 p50 p75 %-Sig %-Sig
Panel C: Height (non-protein)
All IV 525 0.0170 0.0045 0.0039 0 2
All over-identified IV 448 0.0161 0.0042 0.0033 0 1
CD>1P-val HJ>5 163 0.0136 0.0053 0.0018 0 3
CD>3P-val HJ>5 48 0.0059 0.0028 0.0016 0 0
Panel D: Weight (non-protein)
All IV 525 0.0019 0.0012 0.0005 0 0
All over-identified IV 448 0.0018 0.0012 0.0006 0 0
CD>1P-val HJ>5 347 0.0016 0.0012 0.0006 0 0
CD>3P-val HJ>5 132 0.0013 0.0011 0.0006 0 0
robust when we consider specifications with relatively stronger and more exogenous instruments, according to the tests. Restricting to over-identified specifications in
which HJP-values>0.05 and CD>3, total energy
coeffi-cient estimates are positive and significant 57% of the time. To provide further interpretation of the magnitude of the coefficients, we calculate the median prediction (Section
4.1), taking the median coefficient of the specifications
with CD>3; we calculate the effect of increasing energy
per day by 300 kcal. For Guatemala, this implies a 0.62 cm predicted change in height.
For weight production functions, estimated coeffi-cients on total energy are positive and significant for 36% of specifications, and are never significantly negative.
Specifications with higher CD statistics have larger proportions of positive significant coefficient estimates. Fig. 1B shows that while there are fewer specifications with higher CD statistic levels compared to the height model, for those with stronger instruments, the estimates are generally positive. The median prediction exercise indicates increasing energy intake by 300 kcal per day yields a predicted 620 g change in weight.
Next, we consider the roles of protein and non-protein energy separately in the growth model. Proteins robustly and positively affect growth in height and weight in Guatemala, but the relationship of non-proteins (after controlling for protein) with these anthropometric mea-sures is non-positive.
Table 5
Impact of total energy intake on change in heights and weights, Philippines.
Total energy Distribution of total energy coefficient sig>0 sig<0
# of esp. p25 p50 p75 %-Sig %-Sig
Panel A: Height (SeeFig. 1A)
All IV 602 0.0039 0.0069 0.0166 13 0
All over-identified IV 602 0.0039 0.0069 0.0166 13 0
CD>1P-val HJ>5 313 0.0174 0.0067 0.0147 18 0
CD>3P-val HJ>5 118 0.0035 0.0087 0.0140 37 0
CD>7P-val HJ>5 45 0.0076 0.0098 0.0123 64 0
Panel B: Weight (SeeFig. 1B)
All IV 602 0.0013 0.0044 0.0220 15 0
All over-identified IV 602 0.0013 0.0044 0.0220 15 0
CD>1P-val HJ>5 284 0.0013 0.0058 0.0229 7 0
CD>3P-val HJ>5 65 0.0024 0.0063 0.0152 15 0
CD>7P-val HJ>5 15 0.0013 0.0020 0.0031 33 0
SeeTable 3notes.
Table 6
Impact of protein and non-protein energy on change in heights and weights, Philippines.
Protein Distribution of protein coefficient sig>0 sig<0
# of esp. p25 p50 p75 %-Sig %-Sig
Panel A: Height (SeeFig. 2A)
All IV 602 0.6826 1.0868 1.6848 39 0
All over-identified IV 448 0.7758 1.1194 1.7353 46 0
CD>1P-val HJ>5 248 0.8633 1.1247 1.4188 77 0
CD>3P-val HJ>5 16 0.6947 0.9324 1.0274 100 0
Panel B: Weight (SeeFig. 2B)
All IV 602 0.2972 0.3887 0.4818 48 0
All Over-Identified IV 448 0.3145 0.3991 0.4813 56 0
CD>1P-val HJ>5 242 0.3185 0.3766 0.4406 90 0
CD>3P-val HJ>5 16 0.2631 0.2929 0.3110 100 0
Non-protein Distribution of non-protein coefficient sig>0 sig<0
# of esp. p25 p50 p75 %-Sig %-Sig
Panel C: Height (SeeFig. 3A)
All IV 602 0.2792 0.1739 0.0943 0 32
All over-identified IV 448 0.2795 0.1789 0.1110 0 39
CD>1P-val HJ>5 248 0.2362 0.1777 0.1269 0 67
CD>3P-val HJ>5 16 0.1564 0.1283 0.0912 0 88
Panel D: Weight (SeeFig. 3B)
All IV 602 0.0754 0.0592 0.0433 0 38
All over-identified IV 448 0.0748 0.0606 0.0463 0 47
CD>1P-val HJ>5 242 0.0676 0.0577 0.0475 0 80
CD>3P-val HJ>5 16 0.0460 0.0433 0.0379 0 100
Panel A ofTable 4(andFig. 2A) shows that for 53% of all specifications, protein coefficient estimates are positive
and significant. In specifications with CD>3, the estimates
are always positive and significant. In specifications with
stronger instruments, the estimated coefficient dispersion (i.e., the distance between the 25th and 75th percentiles)
decreases; for specifications with CD>1 the ratio of the
coefficients in the 75th and 25th percentiles is 1.3, while
[(Fig._1)TD$FIG]
Fig. 1.Total energy coefficients. (A) Change in height: total energy coefficients. (B) Change in weight: total energy coefficients. E. Puentes et al. / Economics and Human Biology 22 (2016) 65–81
for the specifications with CD>3 the ratio is 1.06. Our median prediction exercise indicates that if protein were to increase by 10 g per day, the predicted change in height is 0.39 cm.
For weight change (Panel B of Table 4andFig. 2B),
we find an even more robust pattern for proteins. In nearly all specifications (92%), protein coefficient esti-mates are positive and significant, and for specifications
[(Fig._2)TD$FIG]
Fig. 2.Protein coefficients. (A) Change in height: protein coefficients. (B) Change in weight: protein coefficients.
with CD>1, they are always positive and significant. For all specifications, the estimate at the 75th percentile is only 1.2 times larger than that at the 25th percentile. This pattern of stability and significance of coefficient
estimates also can be seen in Fig. 2B where the
dispersion of the estimated coefficients is small, and there is a clear pattern of positive and significant effects of protein intake on weight growth. An increment in protein intake of 10 g per day results in a predicted 195 g change in weight.
[(Fig._3)TD$FIG]
Fig. 3.Non-protein coefficients. (A) Change in height: non-protein coefficients. (B) Change in weight: non-protein coefficients. E. Puentes et al. / Economics and Human Biology 22 (2016) 65–81
By contrast, there is little evidence that energy from non-proteins affects changes in height and weight. Panel C inTable 4andFig. 3A show that for Guatemala, in nearly all cases (98%) the estimated coefficient is insignificant in the height model. For the weight production function (Panel D of Table 4 and Fig. 3B), the point estimates are never significant.
4.3. Philippines
Table 5 shows the distribution of the total energy
coefficient estimates for the Philippines andFig. 1A and B
the corresponding coefficients and confidence intervals for
specifications with CD>1. As in Guatemala, positive
relations are most evident for specifications with relatively stronger and more exogenous instruments. The positive impacts of total energy on height and weight are consistent with those found under somewhat stronger identification
assumptions and using the same data, byLiu et al. (2009)
andde Cao (2015).
Across all specifications summarized in the Panel A of Table 5, 13% have positive and significant coefficient
estimates (p<0.05), while none have negative and
statisti-cally significant estimates. Restricting results to the 45
specifications with HJ testP-values>0.05 and CD>7, 64%
of estimated total energy coefficients are positive and significant. Specifications with higher CD statistics tend to have more concentrated coefficient estimate distributions. If daily energy intake increases by 300 kcal the predicted change in height is 0.18 cm.
For weight, evidence is similar regarding the role of total
energy. The bottom panel ofTable 5indicates that for 15%
of all the specifications in the Philippines, the estimated coefficient on total energy is positive and significant and never negative and significant. Specifications with the highest CD statistics tend to have larger shares of positive and significant coefficient estimates. Our median prediction results in a predicted change in weight of 37 g.
Panel A ofTable 6(andFig. 2A) shows that for 39% of all
specifications, protein coefficient estimates are positive and significant. While there are fewer specifications with strong instruments than in Guatemala, for specifications
with CD>3, 100% of the coefficient estimates are positive
and significant. In specifications with stronger instru-ments, the estimated coefficients dispersion decreases. Increasing protein consumption by 10 g per day is predicted to result in a 2.24 cm change in height.
For all specifications (Panel B ofTable 6andFig. 2B),
48% of estimated coefficients on protein for weight are positive and significant – 100% in specifications with
CD>3. Similar to Guatemala, coefficient estimate
disper-sion decreases with stronger instruments. Increasing protein consumption by 10 g per day results in a predicted 703 g change in weight.
Somewhat surprisingly, non-protein intakes are gener-ally negatively related to both height and weight gain. For
height, Panel C of Table 6 reports that 88% of the
specifications with the strongest instruments (CD>3) yield
negative and significant estimated coefficients. For weight, 100% of estimates in specifications with the strongest instruments are negative and significant.
These findings for non-protein energy for the
Philippines are somewhat counter-intuitive, because they suggest that such energy intakes are detrimental to growth. Most individual foods (including those consumed in these regions during the study periods), however, include both proteins and non-proteins and virtually all diets do. Consequently, it is unlikely that actual intakes would change in a fashion that increased energy from non-proteins while simultaneously holding non-proteins constant. Since Filipino children’s diets included both intakes, on net any negative effects of other macronutrient sources would have been partly or fully offset by protein effects. For example, not including breastmilk, at age 6 months, 93% of children had some protein consumption and from ages 14 to 24 months, all did. Moreover, at age 6 months 75% of children are breastfed, which also provides protein intakes.
In Section4.5, we show that the model predicts that a
dietary change (relatively rich in proteins but with some energy from other sources) indeed has positive effects on height and weight, despite negative coefficient estimates on non-proteins.
There are several potential explanations for the finding that non-proteins are less robustly related to anthropo-metrics than proteins. First, it is possible that energy from macronutrients other than proteins do not affect height and weight, at least aggregating the other macronutrients as we do. Second, it may be that non-linearities are not captured. For instance, it could happen that carbohydrates and fat need some proteins to have an effect on anthropometrics—if protein intakes are zero or very low, other intakes would not affect height and weight. Third, dietary changes after children stop breastfeeding can result in poorer quality diets, especially poor quality of carbohydrates and low micronutrient density, weakening any potential link to anthropometrics. Fourth, the available instruments simply may not be powerful enough to detect effects of other macronutrients; protein and non-protein intakes are highly correlated (even before instrumenta-tion), making it difficult econometrically to identify their distinct effects; in that sense, Guatemala greatly benefits from the experimental Atole intervention, which provides a clear and strong exogenous variation for protein, though it is less powerful for other macronutrients.
4.4. Effects of other inputs and controls
for weight in both samples, suggesting that diarrhea has detrimental effects on weight gain as generally found in the literature. The coefficient estimates for breastfeeding are positive and mostly significant for Guatemala. In the Philippines, the coefficient estimates generally show a positive association between breastfeeding and height while the associations between breastfeeding and weight
show no consistent pattern, similar to findings fromAdair
and Popkin (1996).21
4.5. Counterfactual exercise: increasing nutritional intakes
We next simulate the full effects of additional protein and non-protein intakes on child height and weight for the Philippines, complementing the simpler median predic-tions we used when interpreting individual coefficients.
From the set of specifications with HJP-values>0.05, we
select the specification with the highest CD. The simulation is based on adding one egg per week to a child’s diet, assuming no other changes in diet and no change in diarrhea. Eggs are good for such simulations. They were widely available in the localities where these studies are situated and are easily consumed by infants. They not only contain highly bioavailable protein, but also contain energy from other macronutrients, similar to many other naturally protein-rich foods. A medium (44 g), whole raw egg contains on average 5.5 g of protein and 40.9 calories
from non-protein.22,23Based on our parameter estimates, a
child who consumed an additional egg per week on top of existing diet, for 18 months – from 6 to 24 months of age – would gain an additional 0.72 cm in height and 265 grams in weight.
5. Conclusions
Arimond and Ruel (2004) described associations between children’s dietary diversity and their height. We build on their insights, examining effects of diet and particularly diet composition on height and weight growth for children between ages 6 and 24 months, giving special attention to differences between diets rich and poor in proteins. We improve upon previous literature by making weaker identifying assumptions, considering two impor-tant anthropometric measures—height and weight, inves-tigating the robustness of our results to the use of a number of different instruments, and separately investi-gating the effects of energy from proteins and from non-proteins while controlling for breastfeeding and diarrhea. We take advantage of two rich databases, one for Guatemala and the other for the Philippines, which have longitudinal information on height, weight, and protein and energy intakes with high frequencies of observations. IV estimation strategies are used to overcome endogeneity and measurement error problems, using food prices and, in
the case of Guatemala, a randomized nutritional interven-tion, as instruments. Because there are many instruments and instrument combinations available, we present results that comprehensively summarize these combinations rather than selecting only a single set of instruments. Our findings indicate that increasing energy intake increases both height and weight in both countries. But the source of that energy, protein versus non-protein, matters. In these poor populations characterized by high levels of chronic undernutrition, increases in protein intake drive increases in child height and weight.
These results provide evidence on an important puzzle in the literature while pointing to possible modifications to interventions designed to improve children’s nutritional
status. A systematic review byManley et al. (2013)using
meta-analysis techniques shows that while the average impact of income transfers from social protection pro-grams on height-for-age is positive, effect sizes are small and not statistically significant. If households use these transfers largely to increase the quantity of calories consumed, if the increases in protein consumption is small in magnitude, or if these proteins are not allocated to children, then our results suggest that such transfers will
have little impact on child height—precisely whatManley
et al. (2013)find.Headey and Hoddinott (2015)examine impacts of Green Revolution-induced increases in rice productivity on children’s anthropometric status. They find no impact of these on child height, results also consistent with what we observe here. Our findings, in conjunction with these other studies, suggest that inter-ventions designed to increase household incomes may only improve children’s nutritional status when they are linked to mechanisms that also improve the quality of children’s diets. Such interventions, e.g., linking nutritional behavior change communication to social protection interventions or ‘‘nutrition-sensitive agriculture’’ await further study.
Funding
The authors thank Grand Challenges Canada (Grant 0072-03), Bill and Melinda Gates Foundation (Global Health Grant OPP1032713), and the Eunice Shriver Kennedy National Institute of Child Health and Develop-ment (Grant R01 HD070993) for Financial Support. The funders have no involvement in the analysis and interpre-tation of the data, writing of the paper, or the decision to submit the paper for publication.
Conflict of interest
There are no conflicts of interest.
Acknowledgements
This version of the paper has benefited with comments made by two anonymous referees and participants of the seminars at LACEA, PAA and University of Pennsylvania. 21All results available on request.
22Agricultural Research Service of the United States Department of Agriculture. http://ndb.nal.usda.gov/ndb/foods/show/112 accessed on 17th September 2014.
23
If households were to purchase the eggs, the cost would have been
